ESTRO 2023 - Abstract Book

S1892

Digital Posters

ESTRO 2023

Conclusion Radiomic features show a significant range of variability across respiratory phases. In addition, as our results suggest, not only breathing but also the application of specific filtering techniques can affect features stability. While the impact of COVs in clinical prognostic modelling is being assessed by our group, we can affirm that these preliminary results have shed a light on the potentials of implementing 4D-based analysis in radiomic studies for ES-NSCLC.

PO-2109 Impact of normalisation methods for longitudinal MR images on radiomic features.

A. Rankin 1 , M. Aznar 1 , A. Davey 1 , R. Portner 2 , A. McWilliam 1

1 University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom; 2 Lancashire Teaching Hospitals NHS Foundation Trust, Clinical Oncology, Preston, United Kingdom Purpose or Objective Pre-processing steps for extracting radiomic features from MR images have been established in the literature. However, with the introduction of MR-guided radiotherapy (MRGRT), temporally dense datasets are becoming more available and promise to provide greater detail on biological processes during treatment. It is unclear how well-established pre-processing techniques will map into this new domain. In this work, we compare 7 potential normalisation techniques on longitudinal feature selection in MRGRT and present a novel feature selection methodology for longitudinal data. Materials and Methods All treatment images of 20 patients with prostate cancer receiving 36 Gy in 5 fractions were collected. All patients underwent 6-months ADT prior to MRGRT on a 1.5T MR-Linac. • Histogram matching (HM) to 1) inter-patient to a selected template image and 2) intra-patient to fraction 1 image and 3) combination, intra-patient to fraction 1 followed by inter-patient to a template patient. • Intensity rescaling using median signal in 3 ROIs (Fig 1a). Each image was rescaled so the median signal in the ROI was equal to fraction 1. Two out-of-field ROIs were defined in the psoas and glute muscles. Additionally, a region in the prostate was selected. The entire prostate was contoured by an expert clinician and a small volume in the glute and psoas muscles was defined by a PhD student with clinical guidance. In total, 87 features (FTs) were extracted using pyRadiomics. FTs at fraction 1 were correlated with volume and were removed if Spearman correlation > 0.6. Next, redundant FTs (i.e. correlated with another FT) were eliminated using cross-correlation. For each patient, longitudinal-FT pair correlations were calculated and those with correlation > 0.6 were removed. The remaining FTs were ranked for each normalisation across all patients and the 5 most common FTs for each normalisation were then selected. Seven image normalisation techniques were implemented: • Finally, raw images were analysed (i.e. no normalisation).